Yandex

Binary TreesBinary Trees36

  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Postorder Traversal of a Binary Tree Using Recursion
  4. 4Postorder Traversal of a Binary Tree using Iteration
  5. 5Level Order Traversal of a Binary Tree using Recursion
  6. 6Level Order Traversal of a Binary Tree using Iteration
  7. 7Reverse Level Order Traversal of a Binary Tree using Iteration
  8. 8Reverse Level Order Traversal of a Binary Tree using Recursion
  9. 9Find Height of a Binary Tree
  10. 10Find Diameter of a Binary Tree
  11. 11Find Mirror of a Binary Tree - Todo
  12. 12Inorder Traversal of a Binary Tree using Recursion
  13. 13Inorder Traversal of a Binary Tree using Iteration
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree

GraphsGraphs46

  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

TriesTries1

Disjoint Set (Union-Find)
with Union by Rank, Size and Path Compression



Problem Statement

The Disjoint Set (also called Union-Find) is a data structure used to manage a collection of non-overlapping sets. It supports two main operations:

  • Find: Determine which set a particular element belongs to.
  • Union: Merge two sets into a single set.

Disjoint Set is particularly useful in problems involving connected components, cycle detection in graphs, and Kruskal's algorithm for Minimum Spanning Tree.

To improve efficiency, it uses two optimizations:

  • Union by Rank or Union by Size: Always attach the smaller tree under the root of the larger tree.
  • Path Compression: Flatten the tree whenever Find is used, making future queries faster.

Examples

Operation Effect Description
find(3) Returns the representative of the set containing 3 If 3 is part of the set with root 1, find(3) returns 1
union(1, 2) Merges the sets containing 1 and 2 Uses rank/size to decide which root becomes the parent
find(3) after path compression Directly points to root Improves future queries by reducing depth
union(1, 3) May not change structure if both already connected No change if already in same set

Solution

The Disjoint Set data structure efficiently handles union and find operations on dynamic partitions of a set. Initially, each element is its own parent, forming a singleton set. The goal is to repeatedly merge sets and answer queries about element connectivity or group membership.

Union by Rank/Size: When merging two sets, we attach the root of the smaller set (by height or size) to the root of the larger set. This avoids increasing the height of the tree unnecessarily, leading to faster find operations.

Path Compression: During the Find operation, we recursively make every node on the path point directly to the root. This flattens the structure of the tree and drastically reduces the time complexity of future Find operations.

These optimizations together reduce the time complexity of each operation nearly to O(α(n)), where α is the inverse Ackermann function, which grows extremely slowly — effectively constant for practical input sizes.

Use cases include Kruskal’s MST algorithm, checking if two elements are in the same connected component, detecting cycles in undirected graphs, and network connectivity problems.

Algorithm Steps

  1. Initialization: Set each node as its own parent and rank/size as 1.
  2. Find(x):
    1. If x is not the parent of itself, recursively call Find on its parent.
    2. Apply path compression by setting the parent of x to the result of Find.
  3. Union(x, y):
    1. Find roots of both x and y.
    2. If they are the same, no union is needed (already connected).
    3. Else, attach the smaller rank/size tree under the larger one.
    4. If ranks are equal, arbitrarily choose one and increment its rank.

Code

JavaScript
class DisjointSet {
  constructor(n) {
    this.parent = Array.from({ length: n }, (_, i) => i);
    this.rank = Array(n).fill(0);
  }

  find(x) {
    if (this.parent[x] !== x) {
      this.parent[x] = this.find(this.parent[x]); // Path Compression
    }
    return this.parent[x];
  }

  union(x, y) {
    let rootX = this.find(x);
    let rootY = this.find(y);

    if (rootX === rootY) return;

    // Union by Rank
    if (this.rank[rootX] < this.rank[rootY]) {
      this.parent[rootX] = rootY;
    } else if (this.rank[rootX] > this.rank[rootY]) {
      this.parent[rootY] = rootX;
    } else {
      this.parent[rootY] = rootX;
      this.rank[rootX]++;
    }
  }
}

const ds = new DisjointSet(5);
ds.union(0, 1);
ds.union(1, 2);
console.log("Find(2):", ds.find(2)); // Should be 0
console.log("Find(3):", ds.find(3)); // Should be 3 (not connected)


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